BACKGROUND: Expert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure. METHODS: We retrospectively validated the statistical model used by g2p-THEO in approximately 7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega. RESULTS: The difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P < .001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed. CONCLUSION: Finding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.org.
BACKGROUND: Expert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure. METHODS: We retrospectively validated the statistical model used by g2p-THEO in approximately 7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega. RESULTS: The difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P < .001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed. CONCLUSION: Finding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.org.
Authors: Mattia C F Prosperi; Michal Rosen-Zvi; André Altmann; Maurizio Zazzi; Simona Di Giambenedetto; Rolf Kaiser; Eugen Schülter; Daniel Struck; Peter Sloot; David A van de Vijver; Anne-Mieke Vandamme; Anders Sönnerborg Journal: PLoS One Date: 2010-10-29 Impact factor: 3.240
Authors: Soo-Yon Rhee; Jose Luis Blanco; Tommy F Liu; Iñaki Pere; Rolf Kaiser; Maurizio Zazzi; Francesca Incardona; William Towner; Josep Maria Gatell; Andrea De Luca; W Jeffrey Fessel; Robert W Shafer Journal: AIDS Res Ther Date: 2012-05-03 Impact factor: 2.250
Authors: Gerard J P van Westen; Alwin Hendriks; Jörg K Wegner; Adriaan P Ijzerman; Herman W T van Vlijmen; Andreas Bender Journal: PLoS Comput Biol Date: 2013-02-21 Impact factor: 4.475
Authors: Mattia C F Prosperi; Simona Di Giambenedetto; Iuri Fanti; Genny Meini; Bianca Bruzzone; Annapaola Callegaro; Giovanni Penco; Patrizia Bagnarelli; Valeria Micheli; Elisabetta Paolini; Antonio Di Biagio; Valeria Ghisetti; Massimo Di Pietro; Maurizio Zazzi; Andrea De Luca Journal: BMC Med Inform Decis Mak Date: 2011-06-14 Impact factor: 2.796
Authors: Gerard J P van Westen; Jörg K Wegner; Peggy Geluykens; Leen Kwanten; Inge Vereycken; Anik Peeters; Adriaan P Ijzerman; Herman W T van Vlijmen; Andreas Bender Journal: PLoS One Date: 2011-11-23 Impact factor: 3.240